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perilouswithadollarsign/cstrike15_src | f82112a2388b841d72cb62ca48ab1846dfcc11c8 | thirdparty/protobuf-2.5.0/python/google/protobuf/internal/python_message.py | python | _ExtensionDict.__init__ | (self, extended_message) | extended_message: Message instance for which we are the Extensions dict. | extended_message: Message instance for which we are the Extensions dict. | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/client/timeline.py | python | _ChromeTraceFormatter.emit_obj_create | (self, category, name, timestamp, pid, tid, object_id) | Adds an object creation event to the trace.
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category: The event category as a string.
name: The event name as a string.
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category: The event category as a string.
name: The event name as a string.
timestamp: The timestamp of this event as a long integer.
pid: Identifier of the process generating this event as an integer.
tid: Identifier of the thread generating this event as an integer.
object_id: Identifier of the object as an integer.
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event = self._create_event('N', category, name, pid, tid, timestamp)
event['id'] = object_id
self._events.append(event) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | TextCtrl.Create | (*args, **kwargs) | return _controls_.TextCtrl_Create(*args, **kwargs) | Create(self, Window parent, int id=-1, String value=EmptyString,
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/client/session.py | python | BaseSession.close | (self) | Closes this session.
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"""
if self._created_with_new_api:
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tf_session.TF_CloseDeprecatedSession(self._session, status) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/compat/__init__.py | python | is_platform_linux | () | return sys.platform == "linux2" | Checking if the running platform is linux.
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/pickle.py | python | Pickler.clear_memo | (self) | Clears the pickler's "memo".
The memo is the data structure that remembers which objects the
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The memo is the data structure that remembers which objects the
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verilog-to-routing/vtr-verilog-to-routing | d9719cf7374821156c3cee31d66991cb85578562 | vtr_flow/scripts/python_libs/vtr/task.py | python | Job.arch | (self) | return self._arch | return the architecture file name of the job | return the architecture file name of the job | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py | python | choose_shape_attr_or_api | (attr_shape, api_shape, idx=None) | return attr_shape if idx is None else attr_shape[idx] | Input can be attribute `x.shape` or api `shape(x)`, this function
chooses which one to return to use in dy2stat.
Note: sometimes users write `x.shape[3]`, so attr_shape can be an integer. | Input can be attribute `x.shape` or api `shape(x)`, this function
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Note: sometimes users write `x.shape[3]`, so attr_shape can be an integer.
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if api_shape is None:
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/threading.py | python | _Condition.notifyAll | (self) | Wake up all threads waiting on this condition.
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/utils/vim-lldb/python-vim-lldb/vim_panes.py | python | PaneLayout.havePane | (self, name) | return name in self.panes | Returns true if name is a registered pane, False otherwise | Returns true if name is a registered pane, False otherwise | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/delete-and-earn.py | python | Solution.deleteAndEarn | (self, nums) | return val_i | :type nums: List[int]
:rtype: int | :type nums: List[int]
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:type nums: List[int]
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vals = [0] * 10001
for num in nums:
vals[num] += num
val_i, val_i_1 = vals[0], 0
for i in xrange(1, len(vals)):
val_i_1, val_i_2 = val_i, val_i_1
val_i = max(vals[i] + val_i_2, val_i_1)
return val_i | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/io.py | python | open_a_file_dialog | (parent=None, default_suffix=None, directory=None, file_filter=None, accept_mode=None,
file_mode=None) | return filename | Open a dialog asking for a file location and name to and return it
:param parent: QWidget; The parent QWidget of the created dialog
:param default_suffix: String; The default suffix to be passed
:param directory: String; Directory to which the dialog will open
:param file_filter: String; The filter name and file type e.g. "Python files (*.py)"
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:param accept_mode: enum AcceptMode; Defines the AcceptMode of the dialog, check QFileDialog Class for details
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global _LAST_SAVE_DIRECTORY
dialog = QFileDialog(parent)
# It is the intention to only save the user's last used directory until workbench is restarted similar to other
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if _LAST_SAVE_DIRECTORY is not None and directory is None:
dialog.setDirectory(_LAST_SAVE_DIRECTORY)
elif directory is not None:
dialog.setDirectory(directory)
else:
dialog.setDirectory(os.path.expanduser("~"))
if file_filter is not None:
dialog.setFilter(QDir.Files)
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dialog.setDefaultSuffix(default_suffix)
if file_mode is not None:
dialog.setFileMode(file_mode)
if accept_mode is not None:
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# Connect the actual filename setter
dialog.fileSelected.connect(_set_last_save)
# Wait for dialog to finish before allowing continuation of code
if dialog.exec_() == QDialog.Rejected:
return None
filename = _LAST_SAVE_DIRECTORY
# Make sure that the _LAST_SAVE_DIRECTORY is set as a directory
if _LAST_SAVE_DIRECTORY is not None and not os.path.isdir(_LAST_SAVE_DIRECTORY):
# Remove the file for last directory
_LAST_SAVE_DIRECTORY = os.path.dirname(os.path.abspath(_LAST_SAVE_DIRECTORY))
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | FontMapper.__init__ | (self, *args, **kwargs) | __init__(self) -> FontMapper | __init__(self) -> FontMapper | [
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xiaolonw/caffe-video_triplet | c39ea1ad6e937ccf7deba4510b7e555165abf05f | scripts/cpp_lint.py | python | CheckEmptyBlockBody | (filename, clean_lines, linenum, error) | Look for empty loop/conditional body with only a single semicolon.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
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filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
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# Search for loop keywords at the beginning of the line. Because only
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#
# We also check "if" blocks here, since an empty conditional block
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line = clean_lines.elided[linenum]
matched = Match(r'\s*(for|while|if)\s*\(', line)
if matched:
# Find the end of the conditional expression
(end_line, end_linenum, end_pos) = CloseExpression(
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if end_pos >= 0 and Match(r';', end_line[end_pos:]):
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_ACT_SetTimeout_REQUEST.initFromTpm | (self, buf) | TpmMarshaller method | TpmMarshaller method | [
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""" TpmMarshaller method """
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/req/req_install.py | python | _strip_postfix | (req) | return req | Strip req postfix ( -dev, 0.2, etc ) | Strip req postfix ( -dev, 0.2, etc ) | [
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Strip req postfix ( -dev, 0.2, etc )
"""
# FIXME: use package_to_requirement?
match = re.search(r'^(.*?)(?:-dev|-\d.*)$', req)
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/polynomial/laguerre.py | python | lagroots | (c) | return r | Compute the roots of a Laguerre series.
Return the roots (a.k.a. "zeros") of the polynomial
.. math:: p(x) = \\sum_i c[i] * L_i(x).
Parameters
----------
c : 1-D array_like
1-D array of coefficients.
Returns
-------
out : ndarray
Array of the roots of the series. If all the roots are real,
then `out` is also real, otherwise it is complex.
See Also
--------
polyroots, legroots, chebroots, hermroots, hermeroots
Notes
-----
The root estimates are obtained as the eigenvalues of the companion
matrix, Roots far from the origin of the complex plane may have large
errors due to the numerical instability of the series for such
values. Roots with multiplicity greater than 1 will also show larger
errors as the value of the series near such points is relatively
insensitive to errors in the roots. Isolated roots near the origin can
be improved by a few iterations of Newton's method.
The Laguerre series basis polynomials aren't powers of `x` so the
results of this function may seem unintuitive.
Examples
--------
>>> from numpy.polynomial.laguerre import lagroots, lagfromroots
>>> coef = lagfromroots([0, 1, 2])
>>> coef
array([ 2., -8., 12., -6.])
>>> lagroots(coef)
array([ -4.44089210e-16, 1.00000000e+00, 2.00000000e+00]) | Compute the roots of a Laguerre series. | [
"Compute",
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"."
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"""
Compute the roots of a Laguerre series.
Return the roots (a.k.a. "zeros") of the polynomial
.. math:: p(x) = \\sum_i c[i] * L_i(x).
Parameters
----------
c : 1-D array_like
1-D array of coefficients.
Returns
-------
out : ndarray
Array of the roots of the series. If all the roots are real,
then `out` is also real, otherwise it is complex.
See Also
--------
polyroots, legroots, chebroots, hermroots, hermeroots
Notes
-----
The root estimates are obtained as the eigenvalues of the companion
matrix, Roots far from the origin of the complex plane may have large
errors due to the numerical instability of the series for such
values. Roots with multiplicity greater than 1 will also show larger
errors as the value of the series near such points is relatively
insensitive to errors in the roots. Isolated roots near the origin can
be improved by a few iterations of Newton's method.
The Laguerre series basis polynomials aren't powers of `x` so the
results of this function may seem unintuitive.
Examples
--------
>>> from numpy.polynomial.laguerre import lagroots, lagfromroots
>>> coef = lagfromroots([0, 1, 2])
>>> coef
array([ 2., -8., 12., -6.])
>>> lagroots(coef)
array([ -4.44089210e-16, 1.00000000e+00, 2.00000000e+00])
"""
# c is a trimmed copy
[c] = pu.as_series([c])
if len(c) <= 1 :
return np.array([], dtype=c.dtype)
if len(c) == 2 :
return np.array([1 + c[0]/c[1]])
m = lagcompanion(c)
r = la.eigvals(m)
r.sort()
return r | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py2/google/protobuf/internal/decoder.py | python | _DecodeUnknownField | (buffer, pos, wire_type) | return (data, pos) | Decode a unknown field. Returns the UnknownField and new position. | Decode a unknown field. Returns the UnknownField and new position. | [
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"""Decode a unknown field. Returns the UnknownField and new position."""
if wire_type == wire_format.WIRETYPE_VARINT:
(data, pos) = _DecodeVarint(buffer, pos)
elif wire_type == wire_format.WIRETYPE_FIXED64:
(data, pos) = _DecodeFixed64(buffer, pos)
elif wire_type == wire_format.WIRETYPE_FIXED32:
(data, pos) = _DecodeFixed32(buffer, pos)
elif wire_type == wire_format.WIRETYPE_LENGTH_DELIMITED:
(size, pos) = _DecodeVarint(buffer, pos)
data = buffer[pos:pos+size].tobytes()
pos += size
elif wire_type == wire_format.WIRETYPE_START_GROUP:
(data, pos) = _DecodeUnknownFieldSet(buffer, pos)
elif wire_type == wire_format.WIRETYPE_END_GROUP:
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raise _DecodeError('Wrong wire type in tag.')
return (data, pos) | [
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calamares/calamares | 9f6f82405b3074af7c99dc26487d2e46e4ece3e5 | src/modules/bootloader/main.py | python | is_btrfs_root | (partition) | return partition["mountPoint"] == "/" and partition["fs"] == "btrfs" | Returns True if the partition object refers to a btrfs root filesystem
:param partition: A partition map from global storage
:return: True if btrfs and root, False otherwise | Returns True if the partition object refers to a btrfs root filesystem | [
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""" Returns True if the partition object refers to a btrfs root filesystem
:param partition: A partition map from global storage
:return: True if btrfs and root, False otherwise
"""
return partition["mountPoint"] == "/" and partition["fs"] == "btrfs" | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/decimal.py | python | Context._regard_flags | (self, *flags) | Stop ignoring the flags, if they are raised | Stop ignoring the flags, if they are raised | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/traceable_stack.py | python | TraceableStack.__len__ | (self) | return len(self._stack) | Return number of items on the stack, and used for truth-value testing. | Return number of items on the stack, and used for truth-value testing. | [
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"""Return number of items on the stack, and used for truth-value testing."""
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/io/loader.py | python | filename_to_type | (name) | Returns one Klampt type represented by the given filename's
extension.
If the file is a dynamic type (.xml or .json), just 'xml' or 'json' is
returned because the type will need to be determined after parsing the
file.
If the type is ambiguous (like .obj), the first type in EXTENSION_TO_TYPES
is returned.
Returns:
str: The Klamp't type | Returns one Klampt type represented by the given filename's
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"""Returns one Klampt type represented by the given filename's
extension.
If the file is a dynamic type (.xml or .json), just 'xml' or 'json' is
returned because the type will need to be determined after parsing the
file.
If the type is ambiguous (like .obj), the first type in EXTENSION_TO_TYPES
is returned.
Returns:
str: The Klamp't type
"""
fileName, fileExtension = os.path.splitext(name)
fileExtension = fileExtension.lower()
if fileExtension == '.xml':
return 'xml' #dynamic loading
elif fileExtension == '.json':
return 'json' #dynamic loading
elif fileExtension in EXTENSION_TO_TYPES:
ftypes = EXTENSION_TO_TYPES[fileExtension]
if len(ftypes) > 1 and fileExtension not in ['.path'] and (ftypes[0] != 'Geometry3D' and len(ftypes) > 2):
warnings.warn("loader.filename_to_type(): filename {} is ambiguous, matches types {}".format(name,', '.join(ftypes)))
return ftypes[0]
else:
raise RuntimeError("Cannot determine type of object from filename "+name) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/hypertreelist.py | python | HyperTreeList.DoGetBestSize | (self) | return wx.Size(200, 200) | Gets the size which best suits the window: for a control, it would be the
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as it would have after a call to `Fit()`.
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as it would have after a call to `Fit()`.
:note: Overridden from :class:`PyControl`.
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# something is better than nothing...
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/training/queue_runner.py | python | QueueRunner.create_threads | (self, sess, coord=None, daemon=False, start=False) | return ret_threads | Create threads to run the enqueue ops.
This method requires a session in which the graph was launched. It creates
a list of threads, optionally starting them. There is one thread for each
op passed in `enqueue_ops`.
The `coord` argument is an optional coordinator, that the threads will use
to terminate together and report exceptions. If a coordinator is given,
this method starts an additional thread to close the queue when the
coordinator requests a stop.
This method may be called again as long as all threads from a previous call
have stopped.
Args:
sess: A `Session`.
coord: Optional `Coordinator` object for reporting errors and checking
stop conditions.
daemon: Boolean. If `True` make the threads daemon threads.
start: Boolean. If `True` starts the threads. If `False` the
caller must call the `start()` method of the returned threads.
Returns:
A list of threads.
Raises:
RuntimeError: If threads from a previous call to `create_threads()` are
still running. | Create threads to run the enqueue ops. | [
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"""Create threads to run the enqueue ops.
This method requires a session in which the graph was launched. It creates
a list of threads, optionally starting them. There is one thread for each
op passed in `enqueue_ops`.
The `coord` argument is an optional coordinator, that the threads will use
to terminate together and report exceptions. If a coordinator is given,
this method starts an additional thread to close the queue when the
coordinator requests a stop.
This method may be called again as long as all threads from a previous call
have stopped.
Args:
sess: A `Session`.
coord: Optional `Coordinator` object for reporting errors and checking
stop conditions.
daemon: Boolean. If `True` make the threads daemon threads.
start: Boolean. If `True` starts the threads. If `False` the
caller must call the `start()` method of the returned threads.
Returns:
A list of threads.
Raises:
RuntimeError: If threads from a previous call to `create_threads()` are
still running.
"""
with self._lock:
if self._runs > 0:
# Already started: no new threads to return.
return []
self._runs = len(self._enqueue_ops)
self._exceptions_raised = []
ret_threads = [threading.Thread(target=self._run, args=(sess, op, coord))
for op in self._enqueue_ops]
if coord:
ret_threads.append(threading.Thread(target=self._close_on_stop,
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for t in ret_threads:
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t.daemon = True
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/dataset/engine/validators.py | python | check_project | (method) | return new_method | check the input arguments of project. | check the input arguments of project. | [
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KhronosGroup/SPIRV-LLVM | 1eb85593f3fe2c39379b9a9b088d51eda4f42b8b | utils/lit/lit/discovery.py | python | find_tests_for_inputs | (lit_config, inputs) | return tests | find_tests_for_inputs(lit_config, inputs) -> [Test]
Given a configuration object and a list of input specifiers, find all the
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"""
find_tests_for_inputs(lit_config, inputs) -> [Test]
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tests to execute.
"""
# Expand '@...' form in inputs.
actual_inputs = []
for input in inputs:
if input.startswith('@'):
f = open(input[1:])
try:
for ln in f:
ln = ln.strip()
if ln:
actual_inputs.append(ln)
finally:
f.close()
else:
actual_inputs.append(input)
# Load the tests from the inputs.
tests = []
test_suite_cache = {}
local_config_cache = {}
for input in actual_inputs:
prev = len(tests)
tests.extend(getTests(input, lit_config,
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if prev == len(tests):
lit_config.warning('input %r contained no tests' % input)
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if lit_config.numErrors:
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sys.exit(2)
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mapreduce/mapreduce/model.py | python | ShardState.find_all_by_mapreduce_state | (cls, mapreduce_state) | Find all shard states for given mapreduce.
Args:
mapreduce_state: MapreduceState instance
Yields:
shard states sorted by shard id. | Find all shard states for given mapreduce. | [
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Args:
mapreduce_state: MapreduceState instance
Yields:
shard states sorted by shard id.
"""
keys = cls.calculate_keys_by_mapreduce_state(mapreduce_state)
i = 0
while i < len(keys):
@db.non_transactional
def no_tx_get(i):
return db.get(keys[i:i+cls._MAX_STATES_IN_MEMORY])
# We need a separate function to so that we can mix non-transactional and
# use be a generator
states = no_tx_get(i)
for s in states:
i += 1
if s is not None:
yield s | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/mac_tool.py | python | MacTool._WritePkgInfo | (self, info_plist) | This writes the PkgInfo file from the data stored in Info.plist. | This writes the PkgInfo file from the data stored in Info.plist. | [
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"""This writes the PkgInfo file from the data stored in Info.plist."""
plist = plistlib.readPlist(info_plist)
if not plist:
return
# Only create PkgInfo for executable types.
package_type = plist['CFBundlePackageType']
if package_type != 'APPL':
return
# The format of PkgInfo is eight characters, representing the bundle type
# and bundle signature, each four characters. If that is missing, four
# '?' characters are used instead.
signature_code = plist.get('CFBundleSignature', '????')
if len(signature_code) != 4: # Wrong length resets everything, too.
signature_code = '?' * 4
dest = os.path.join(os.path.dirname(info_plist), 'PkgInfo')
fp = open(dest, 'w')
fp.write('%s%s' % (package_type, signature_code))
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | Examples/Catalyst/CFullExample2/SampleScripts/feslicescript.py | python | DoCoProcessing | (datadescription) | Callback to do co-processing for current timestep | Callback to do co-processing for current timestep | [
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global coprocessor
# Update the coprocessor by providing it the newly generated simulation data.
# If the pipeline hasn't been setup yet, this will setup the pipeline.
coprocessor.UpdateProducers(datadescription)
# Write output data, if appropriate.
coprocessor.WriteData(datadescription);
# Write image capture (Last arg: rescale lookup table), if appropriate.
coprocessor.WriteImages(datadescription, rescale_lookuptable=False)
# Live Visualization, if enabled.
coprocessor.DoLiveVisualization(datadescription, "localhost", 22222) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/pyprogress.py | python | ProgressGauge.GetGaugeBackground | (self) | return self._background | Returns the gauge background colour. | Returns the gauge background colour. | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/control/motion_generation.py | python | VelocityBoundedMotionGeneration.reset | (self,x0) | Resets the motion generator to the start position x0. | Resets the motion generator to the start position x0. | [
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] | def reset(self,x0):
"""Resets the motion generator to the start position x0."""
self.x = x0
self.v = [0]*len(x0)
self.times = [0]
self.milestones = [self.x]
self.curTime = 0
self.trajTime = 0 | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | PyDataObjectSimple.__init__ | (self, *args, **kwargs) | __init__(self, DataFormat format=FormatInvalid) -> PyDataObjectSimple
wx.PyDataObjectSimple is a version of `wx.DataObjectSimple` that is
Python-aware and knows how to reflect calls to its C++ virtual methods
to methods in the Python derived class. You should derive from this
class and overload `GetDataSize`, `GetDataHere` and `SetData` when you
need to create your own simple single-format type of `wx.DataObject`. | __init__(self, DataFormat format=FormatInvalid) -> PyDataObjectSimple | [
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"""
__init__(self, DataFormat format=FormatInvalid) -> PyDataObjectSimple
wx.PyDataObjectSimple is a version of `wx.DataObjectSimple` that is
Python-aware and knows how to reflect calls to its C++ virtual methods
to methods in the Python derived class. You should derive from this
class and overload `GetDataSize`, `GetDataHere` and `SetData` when you
need to create your own simple single-format type of `wx.DataObject`.
"""
_misc_.PyDataObjectSimple_swiginit(self,_misc_.new_PyDataObjectSimple(*args, **kwargs))
PyDataObjectSimple._setCallbackInfo(self, self, PyDataObjectSimple) | [
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/google/protobuf/internal/containers.py | python | RepeatedCompositeFieldContainer.__delitem__ | (self, key) | Deletes the item at the specified position. | Deletes the item at the specified position. | [
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] | def __delitem__(self, key):
"""Deletes the item at the specified position."""
del self._values[key]
self._message_listener.Modified() | [
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nileshkulkarni/csm | 0e6e0e7d4f725fd36f2414c0be4b9d83197aa1fc | csm/utils/transformations.py | python | identity_matrix | () | return numpy.identity(4) | Return 4x4 identity/unit matrix.
>>> I = identity_matrix()
>>> numpy.allclose(I, numpy.dot(I, I))
True
>>> numpy.sum(I), numpy.trace(I)
(4.0, 4.0)
>>> numpy.allclose(I, numpy.identity(4))
True | Return 4x4 identity/unit matrix. | [
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"/",
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"."
] | def identity_matrix():
"""Return 4x4 identity/unit matrix.
>>> I = identity_matrix()
>>> numpy.allclose(I, numpy.dot(I, I))
True
>>> numpy.sum(I), numpy.trace(I)
(4.0, 4.0)
>>> numpy.allclose(I, numpy.identity(4))
True
"""
return numpy.identity(4) | [
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PrincetonUniversity/athena-public-version | 9c266692b9423743d8e23509b3ab266a232a92d2 | tst/style/cpplint.py | python | _CppLintState.BackupFilters | (self) | Saves the current filter list to backup storage. | Saves the current filter list to backup storage. | [
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"current",
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"list",
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"storage",
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] | def BackupFilters(self):
""" Saves the current filter list to backup storage."""
self._filters_backup = self.filters[:] | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | example/rnn/large_word_lm/model.py | python | rnn | (bptt, vocab_size, num_embed, nhid, num_layers, dropout, num_proj, batch_size) | return outputs, states, trainable_lstm_args, state_names | word embedding + LSTM Projected | word embedding + LSTM Projected | [
"word",
"embedding",
"+",
"LSTM",
"Projected"
] | def rnn(bptt, vocab_size, num_embed, nhid, num_layers, dropout, num_proj, batch_size):
""" word embedding + LSTM Projected """
state_names = []
data = S.var('data')
weight = S.var("encoder_weight", stype='row_sparse')
embed = S.sparse.Embedding(data=data, weight=weight, input_dim=vocab_size,
output_dim=num_embed, name='embed', sparse_grad=True)
states = []
outputs = S.Dropout(embed, p=dropout)
for i in range(num_layers):
prefix = 'lstmp%d_' % i
init_h = S.var(prefix + 'init_h', shape=(batch_size, num_proj), init=mx.init.Zero())
init_c = S.var(prefix + 'init_c', shape=(batch_size, nhid), init=mx.init.Zero())
state_names += [prefix + 'init_h', prefix + 'init_c']
lstmp = mx.gluon.contrib.rnn.LSTMPCell(nhid, num_proj, prefix=prefix)
outputs, next_states = lstmp.unroll(bptt, outputs, begin_state=[init_h, init_c], \
layout='NTC', merge_outputs=True)
outputs = S.Dropout(outputs, p=dropout)
states += [S.stop_gradient(s) for s in next_states]
outputs = S.reshape(outputs, shape=(-1, num_proj))
trainable_lstm_args = []
for arg in outputs.list_arguments():
if 'lstmp' in arg and 'init' not in arg:
trainable_lstm_args.append(arg)
return outputs, states, trainable_lstm_args, state_names | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/html.py | python | HtmlHelpWindow.GetHtmlWindow | (*args, **kwargs) | return _html.HtmlHelpWindow_GetHtmlWindow(*args, **kwargs) | GetHtmlWindow(self) -> HtmlWindow | GetHtmlWindow(self) -> HtmlWindow | [
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"""GetHtmlWindow(self) -> HtmlWindow"""
return _html.HtmlHelpWindow_GetHtmlWindow(*args, **kwargs) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TMemIn_New | (*args) | return _snap.TMemIn_New(*args) | New(TMem Mem) -> PSIn
Parameters:
Mem: TMem const &
TMemIn_New(PMem const & Mem) -> PSIn
Parameters:
Mem: PMem const & | New(TMem Mem) -> PSIn | [
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"""
New(TMem Mem) -> PSIn
Parameters:
Mem: TMem const &
TMemIn_New(PMem const & Mem) -> PSIn
Parameters:
Mem: PMem const &
"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/wizard.py | python | Wizard.ShowPage | (*args, **kwargs) | return _wizard.Wizard_ShowPage(*args, **kwargs) | ShowPage(self, WizardPage page, bool goingForward=True) -> bool | ShowPage(self, WizardPage page, bool goingForward=True) -> bool | [
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"""ShowPage(self, WizardPage page, bool goingForward=True) -> bool"""
return _wizard.Wizard_ShowPage(*args, **kwargs) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/tpu/tpu_embedding_gradient.py | python | create_dummy_table_variables | (tpu_embedding) | return (dummy_table_variables,
variables.variables_initializer(
dummy_table_variables.values(),
name='tpu_embedding_dummy_table_variables_init')) | Create dummy embedding table variables.
The sole purpose of these dummy variables are to trigger gradient
calcuation wrt them so that the gradients wrt activation can be captured
and later sent to TPU embedding.
Args:
tpu_embedding: TPUEmbedding, dummy table variables will be created for use
with tpu_embedding.
Returns:
A tuple of dummy variables and their initializer.
Raises:
RuntimeError: if collection to store gradients already exists and is not
empty. | Create dummy embedding table variables. | [
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"."
] | def create_dummy_table_variables(tpu_embedding):
"""Create dummy embedding table variables.
The sole purpose of these dummy variables are to trigger gradient
calcuation wrt them so that the gradients wrt activation can be captured
and later sent to TPU embedding.
Args:
tpu_embedding: TPUEmbedding, dummy table variables will be created for use
with tpu_embedding.
Returns:
A tuple of dummy variables and their initializer.
Raises:
RuntimeError: if collection to store gradients already exists and is not
empty.
"""
dummy_table_variables = collections.OrderedDict()
for table_id, table in enumerate(tpu_embedding.table_to_features_dict):
dummy_table_variables[table] = (
# Explicitly specifying collections prevents this variable from
# being added to the GLOBAL_VARIABLES collection, so that Saver()
# ignores it.
# But Tensorflow optimizer creates slot variable for these dummy
# variable, e.g. tpu_embedding_dummy_table_variable_mlp_user/Adam{_1},
# which will be in GLOBAL_VARIABLES collection,
variable_scope.get_variable(
'tpu_embedding_dummy_table_variable_{}'.format(table),
dtype=dtypes.float32,
shape=[1],
use_resource=True,
trainable=True,
collections=['tpu_embedding_dummy_table_variables']))
g = ops.get_default_graph()
table_gradients = g.get_collection_ref(
'tpu_embedding_gradients_table_{}'.format(table_id))
if table_gradients:
raise RuntimeError(
'tpu_embedding_gradients_table_{} is not empty.'.format(table_id))
table_gradients.extend(
[None] * len(tpu_embedding.table_to_features_dict[table]))
return (dummy_table_variables,
variables.variables_initializer(
dummy_table_variables.values(),
name='tpu_embedding_dummy_table_variables_init')) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/internals/managers.py | python | BlockManager.quantile | (
self,
axis=0,
consolidate=True,
transposed=False,
interpolation="linear",
qs=None,
numeric_only=None,
) | return SingleBlockManager(
[make_block(values, ndim=1, placement=np.arange(len(values)))], axes[0]
) | Iterate over blocks applying quantile reduction.
This routine is intended for reduction type operations and
will do inference on the generated blocks.
Parameters
----------
axis: reduction axis, default 0
consolidate: boolean, default True. Join together blocks having same
dtype
transposed: boolean, default False
we are holding transposed data
interpolation : type of interpolation, default 'linear'
qs : a scalar or list of the quantiles to be computed
numeric_only : ignored
Returns
-------
Block Manager (new object) | Iterate over blocks applying quantile reduction.
This routine is intended for reduction type operations and
will do inference on the generated blocks. | [
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self,
axis=0,
consolidate=True,
transposed=False,
interpolation="linear",
qs=None,
numeric_only=None,
):
"""
Iterate over blocks applying quantile reduction.
This routine is intended for reduction type operations and
will do inference on the generated blocks.
Parameters
----------
axis: reduction axis, default 0
consolidate: boolean, default True. Join together blocks having same
dtype
transposed: boolean, default False
we are holding transposed data
interpolation : type of interpolation, default 'linear'
qs : a scalar or list of the quantiles to be computed
numeric_only : ignored
Returns
-------
Block Manager (new object)
"""
# Series dispatches to DataFrame for quantile, which allows us to
# simplify some of the code here and in the blocks
assert self.ndim >= 2
if consolidate:
self._consolidate_inplace()
def get_axe(block, qs, axes):
# Because Series dispatches to DataFrame, we will always have
# block.ndim == 2
from pandas import Float64Index
if is_list_like(qs):
ax = Float64Index(qs)
else:
ax = axes[0]
return ax
axes, blocks = [], []
for b in self.blocks:
block = b.quantile(axis=axis, qs=qs, interpolation=interpolation)
axe = get_axe(b, qs, axes=self.axes)
axes.append(axe)
blocks.append(block)
# note that some DatetimeTZ, Categorical are always ndim==1
ndim = {b.ndim for b in blocks}
assert 0 not in ndim, ndim
if 2 in ndim:
new_axes = list(self.axes)
# multiple blocks that are reduced
if len(blocks) > 1:
new_axes[1] = axes[0]
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/ensemble/_bagging.py | python | _generate_indices | (random_state, bootstrap, n_population, n_samples) | return indices | Draw randomly sampled indices. | Draw randomly sampled indices. | [
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NicknineTheEagle/TF2-Base | 20459c5a7fbc995b6bf54fa85c2f62a101e9fb64 | src/thirdparty/protobuf-2.3.0/python/mox.py | python | Mox.UnsetStubs | (self) | Restore stubs to their original state. | Restore stubs to their original state. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/framemanager.py | python | AuiManager.CreateGuideWindows | (self) | Creates the VS2005 HUD guide windows. | Creates the VS2005 HUD guide windows. | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/numpy_ops/np_array_ops.py | python | where | (condition, x=None, y=None) | Raises ValueError if exactly one of x or y is not None. | Raises ValueError if exactly one of x or y is not None. | [
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chuckcho/video-caffe | fc232b3e3a90ea22dd041b9fc5c542f170581f20 | python/caffe/draw.py | python | choose_color_by_layertype | (layertype) | return color | Define colors for nodes based on the layer type. | Define colors for nodes based on the layer type. | [
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color = '#6495ED' # Default
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/matlib.py | python | zeros | (shape, dtype=None, order='C') | return a | Return a matrix of given shape and type, filled with zeros.
Parameters
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shape : int or sequence of ints
Shape of the matrix
dtype : data-type, optional
The desired data-type for the matrix, default is float.
order : {'C', 'F'}, optional
Whether to store the result in C- or Fortran-contiguous order,
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Returns
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out : matrix
Zero matrix of given shape, dtype, and order.
See Also
--------
numpy.zeros : Equivalent array function.
matlib.ones : Return a matrix of ones.
Notes
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If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
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>>> import numpy.matlib
>>> np.matlib.zeros((2, 3))
matrix([[ 0., 0., 0.],
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>>> np.matlib.zeros(2)
matrix([[ 0., 0.]]) | Return a matrix of given shape and type, filled with zeros. | [
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shape : int or sequence of ints
Shape of the matrix
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a.fill(0)
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Place.place_info | (self) | return dict | Return information about the placing options
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/inplace_ops.py | python | alias_inplace_update | (x, i, v) | return _inplace_helper(x, i, v, gen_array_ops.inplace_update) | Applies an inplace update on input x at index i with value v. Aliases x.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/urllib3/packages/six.py | python | _SixMetaPathImporter.is_package | (self, fullname) | return hasattr(self.__get_module(fullname), "__path__") | Return true, if the named module is a package.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | TextAreaBase.ShowPosition | (*args, **kwargs) | return _core_.TextAreaBase_ShowPosition(*args, **kwargs) | ShowPosition(self, long pos) | ShowPosition(self, long pos) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/computation/pytables.py | python | BinOp.conform | (self, rhs) | return rhs | inplace conform rhs | inplace conform rhs | [
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carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | Co-Simulation/PTV-Vissim/vissim_integration/carla_simulation.py | python | CarlaSimulation.destroy_actor | (self, actor_id) | return False | Destroys the given actor. | Destroys the given actor. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py2/jinja2/nodes.py | python | Node.set_ctx | (self, ctx) | return self | Reset the context of a node and all child nodes. Per default the
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/lookup_ops.py | python | InitializableLookupTableBase.init | (self) | return self._init | The table initialization op. | The table initialization op. | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/tbe/square_sum_v1.py | python | _square_sum_v1_tbe | () | return | SquareSumV1 TBE register | SquareSumV1 TBE register | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/external/bazel_tools/third_party/py/gflags/__init__.py | python | DEFINE_string | (name, default, help, flag_values=FLAGS, **args) | Registers a flag whose value can be any string. | Registers a flag whose value can be any string. | [
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"flag",
"whose",
"value",
"can",
"be",
"any",
"string",
"."
] | def DEFINE_string(name, default, help, flag_values=FLAGS, **args):
"""Registers a flag whose value can be any string."""
parser = ArgumentParser()
serializer = ArgumentSerializer()
DEFINE(parser, name, default, help, flag_values, serializer, **args) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/backend.py | python | _get_session | (op_input_list=()) | return session | Returns the session object for the current thread. | Returns the session object for the current thread. | [
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] | def _get_session(op_input_list=()):
"""Returns the session object for the current thread."""
global _SESSION
default_session = ops.get_default_session()
if default_session is not None:
session = default_session
else:
if ops.inside_function():
raise RuntimeError('Cannot get session inside Tensorflow graph function.')
# If we don't have a session, or that session does not match the current
# graph, create and cache a new session.
if (getattr(_SESSION, 'session', None) is None or
_SESSION.session.graph is not _current_graph(op_input_list)):
# If we are creating the Session inside a tf.distribute.Strategy scope,
# we ask the strategy for the right session options to use.
if distribution_strategy_context.has_strategy():
configure_and_create_distributed_session(
distribution_strategy_context.get_strategy())
else:
_SESSION.session = session_module.Session(
config=get_default_session_config())
session = _SESSION.session
return session | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | AnyButton.GetBitmapMargins | (*args, **kwargs) | return _controls_.AnyButton_GetBitmapMargins(*args, **kwargs) | GetBitmapMargins(self) -> Size | GetBitmapMargins(self) -> Size | [
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"""GetBitmapMargins(self) -> Size"""
return _controls_.AnyButton_GetBitmapMargins(*args, **kwargs) | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/distlib/util.py | python | FileOperator.copy_file | (self, infile, outfile, check=True) | Copy a file respecting dry-run and force flags. | Copy a file respecting dry-run and force flags. | [
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] | def copy_file(self, infile, outfile, check=True):
"""Copy a file respecting dry-run and force flags.
"""
self.ensure_dir(os.path.dirname(outfile))
logger.info('Copying %s to %s', infile, outfile)
if not self.dry_run:
msg = None
if check:
if os.path.islink(outfile):
msg = '%s is a symlink' % outfile
elif os.path.exists(outfile) and not os.path.isfile(outfile):
msg = '%s is a non-regular file' % outfile
if msg:
raise ValueError(msg + ' which would be overwritten')
shutil.copyfile(infile, outfile)
self.record_as_written(outfile) | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/gdb/mongo_lock.py | python | MongoDBWaitsForGraph.mongodb_waitsfor_graph | (self, file=None) | GDB in-process python supplement | GDB in-process python supplement | [
"GDB",
"in",
"-",
"process",
"python",
"supplement"
] | def mongodb_waitsfor_graph(self, file=None):
"""GDB in-process python supplement"""
graph = Graph()
try:
thread_dict = get_threads_info(graph=graph)
get_locks(graph=graph, thread_dict=thread_dict, show=False)
graph.remove_nodes_without_edge()
if graph.is_empty():
print("Not generating the digraph, since the lock graph is empty")
return
cycle_message = "# No cycle detected in the graph"
cycle_nodes = graph.detect_cycle()
if cycle_nodes:
cycle_message = "# Cycle detected in the graph nodes %s" % cycle_nodes
if file:
print("Saving digraph to %s" % file)
with open(file, 'w') as f:
f.write(graph.to_graph(nodes=cycle_nodes, message=cycle_message))
print(cycle_message.split("# ")[1])
else:
print(graph.to_graph(nodes=cycle_nodes, message=cycle_message))
except gdb.error as err:
print("Ignoring GDB error '%s' in mongod_deadlock_graph" % str(err)) | [
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H-uru/Plasma | c2140ea046e82e9c199e257a7f2e7edb42602871 | Scripts/Python/xLocTools.py | python | MemberStatusString | () | return PtGetLocalizedString("Neighborhood.PlayerStatus.Member") | returns a string of what type of neighborhood member this person is | returns a string of what type of neighborhood member this person is | [
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] | def MemberStatusString():
"returns a string of what type of neighborhood member this person is"
ageInfo = ptAgeInfoStruct()
ageInfo.setAgeFilename("Neighborhood")
if ptVault().amAgeCzar(ageInfo):
return PtGetLocalizedString("Neighborhood.PlayerStatus.Mayor")
return PtGetLocalizedString("Neighborhood.PlayerStatus.Member") | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | TarFile.addfile | (self, tarinfo, fileobj=None) | Add the TarInfo object `tarinfo' to the archive. If `fileobj' is
given, tarinfo.size bytes are read from it and added to the archive.
You can create TarInfo objects using gettarinfo().
On Windows platforms, `fileobj' should always be opened with mode
'rb' to avoid irritation about the file size. | Add the TarInfo object `tarinfo' to the archive. If `fileobj' is
given, tarinfo.size bytes are read from it and added to the archive.
You can create TarInfo objects using gettarinfo().
On Windows platforms, `fileobj' should always be opened with mode
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"""Add the TarInfo object `tarinfo' to the archive. If `fileobj' is
given, tarinfo.size bytes are read from it and added to the archive.
You can create TarInfo objects using gettarinfo().
On Windows platforms, `fileobj' should always be opened with mode
'rb' to avoid irritation about the file size.
"""
self._check("aw")
tarinfo = copy.copy(tarinfo)
buf = tarinfo.tobuf(self.format, self.encoding, self.errors)
self.fileobj.write(buf)
self.offset += len(buf)
# If there's data to follow, append it.
if fileobj is not None:
copyfileobj(fileobj, self.fileobj, tarinfo.size)
blocks, remainder = divmod(tarinfo.size, BLOCKSIZE)
if remainder > 0:
self.fileobj.write(NUL * (BLOCKSIZE - remainder))
blocks += 1
self.offset += blocks * BLOCKSIZE
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/metrics/python/ops/metric_ops.py | python | streaming_mean_tensor | (values, weights=None, metrics_collections=None,
updates_collections=None, name=None) | Computes the element-wise (weighted) mean of the given tensors.
In contrast to the `streaming_mean` function which returns a scalar with the
mean, this function returns an average tensor with the same shape as the
input tensors.
The `streaming_mean_tensor` function creates two local variables,
`total_tensor` and `count_tensor` that are used to compute the average of
`values`. This average is ultimately returned as `mean` which is an idempotent
operation that simply divides `total` by `count`. To facilitate the estimation
of a mean over a stream of data, the function creates an `update_op` operation
whose behavior is dependent on the value of `weights`. If `weights` is None,
then `update_op` increments `total` with the reduced sum of `values` and
increments `count` with the number of elements in `values`. If `weights` is
not `None`, then `update_op` increments `total` with the reduced sum of the
product of `values` and `weights` and increments `count` with the reduced sum
of weights. In addition to performing the updates, `update_op` also returns
the `mean`.
Args:
values: A `Tensor` of arbitrary dimensions.
weights: An optional set of weights of the same shape as `values`. If
`weights` is not None, the function computes a weighted mean.
metrics_collections: An optional list of collections that `mean`
should be added to.
updates_collections: An optional list of collections that `update_op`
should be added to.
name: An optional variable_op_scope name.
Returns:
mean: A float tensor representing the current mean, the value of `total`
divided by `count`.
update_op: An operation that increments the `total` and `count` variables
appropriately and whose value matches `mean_value`.
Raises:
ValueError: If `weights` is not `None` and its shape doesn't match `values`
or if either `metrics_collections` or `updates_collections` are not a list
or tuple. | Computes the element-wise (weighted) mean of the given tensors. | [
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"-",
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] | def streaming_mean_tensor(values, weights=None, metrics_collections=None,
updates_collections=None, name=None):
"""Computes the element-wise (weighted) mean of the given tensors.
In contrast to the `streaming_mean` function which returns a scalar with the
mean, this function returns an average tensor with the same shape as the
input tensors.
The `streaming_mean_tensor` function creates two local variables,
`total_tensor` and `count_tensor` that are used to compute the average of
`values`. This average is ultimately returned as `mean` which is an idempotent
operation that simply divides `total` by `count`. To facilitate the estimation
of a mean over a stream of data, the function creates an `update_op` operation
whose behavior is dependent on the value of `weights`. If `weights` is None,
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the `mean`.
Args:
values: A `Tensor` of arbitrary dimensions.
weights: An optional set of weights of the same shape as `values`. If
`weights` is not None, the function computes a weighted mean.
metrics_collections: An optional list of collections that `mean`
should be added to.
updates_collections: An optional list of collections that `update_op`
should be added to.
name: An optional variable_op_scope name.
Returns:
mean: A float tensor representing the current mean, the value of `total`
divided by `count`.
update_op: An operation that increments the `total` and `count` variables
appropriately and whose value matches `mean_value`.
Raises:
ValueError: If `weights` is not `None` and its shape doesn't match `values`
or if either `metrics_collections` or `updates_collections` are not a list
or tuple.
"""
with variable_scope.variable_op_scope([values, weights], name, 'mean'):
total = _create_local('total_tensor', shape=values.get_shape())
count = _create_local('count_tensor', shape=values.get_shape())
if weights is not None:
values.get_shape().assert_is_compatible_with(weights.get_shape())
weights = math_ops.to_float(weights)
values = math_ops.mul(values, weights)
num_values = weights
else:
num_values = array_ops.ones_like(values)
total_compute_op = state_ops.assign_add(total, values)
count_compute_op = state_ops.assign_add(count, num_values)
def compute_mean(total, count, name):
non_zero_count = math_ops.maximum(count,
array_ops.ones_like(count),
name=name)
return math_ops.truediv(total, non_zero_count, name=name)
mean = compute_mean(total, count, 'value')
with ops.control_dependencies([total_compute_op, count_compute_op]):
update_op = compute_mean(total, count, 'update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, mean)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return mean, update_op | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/pydoc.py | python | pipepager | (text, cmd) | Page through text by feeding it to another program. | Page through text by feeding it to another program. | [
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] | def pipepager(text, cmd):
"""Page through text by feeding it to another program."""
pipe = os.popen(cmd, 'w')
try:
pipe.write(_encode(text))
pipe.close()
except IOError:
pass | [
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tiny-dnn/tiny-dnn | c0f576f5cb7b35893f62127cb7aec18f77a3bcc5 | third_party/gemmlowp/meta/generators/gemm_MxNxK.py | python | GenerateGemmSwitch1 | (emitter, output_type, aligned) | First level of main switch, choose optimized version on rows leftover. | First level of main switch, choose optimized version on rows leftover. | [
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"."
] | def GenerateGemmSwitch1(emitter, output_type, aligned):
"""First level of main switch, choose optimized version on rows leftover."""
emitter.EmitSwitch('m % 3')
for m_mod in range(0, 3):
emitter.EmitCase(m_mod)
emitter.PushIndent()
GenerateGemmSwitch2(emitter, output_type, aligned, m_mod)
emitter.EmitBreak()
emitter.PopIndent()
emitter.EmitSwitchEnd() | [
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trailofbits/llvm-sanitizer-tutorial | d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99 | llvm/tools/clang/bindings/python/clang/cindex.py | python | Type.get_fields | (self) | return iter(fields) | Return an iterator for accessing the fields of this type. | Return an iterator for accessing the fields of this type. | [
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"""Return an iterator for accessing the fields of this type."""
def visitor(field, children):
assert field != conf.lib.clang_getNullCursor()
# Create reference to TU so it isn't GC'd before Cursor.
field._tu = self._tu
fields.append(field)
return 1 # continue
fields = []
conf.lib.clang_Type_visitFields(self,
callbacks['fields_visit'](visitor), fields)
return iter(fields) | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/MSVSSettings.py | python | ConvertVCMacrosToMSBuild | (s) | return s | Convert the the MSVS macros found in the string to the MSBuild equivalent.
This list is probably not exhaustive. Add as needed. | Convert the the MSVS macros found in the string to the MSBuild equivalent. | [
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] | def ConvertVCMacrosToMSBuild(s):
"""Convert the the MSVS macros found in the string to the MSBuild equivalent.
This list is probably not exhaustive. Add as needed.
"""
if '$' in s:
replace_map = {
'$(ConfigurationName)': '$(Configuration)',
'$(InputDir)': '%(RelativeDir)',
'$(InputExt)': '%(Extension)',
'$(InputFileName)': '%(Filename)%(Extension)',
'$(InputName)': '%(Filename)',
'$(InputPath)': '%(Identity)',
'$(ParentName)': '$(ProjectFileName)',
'$(PlatformName)': '$(Platform)',
'$(SafeInputName)': '%(Filename)',
}
for old, new in replace_map.iteritems():
s = s.replace(old, new)
s = FixVCMacroSlashes(s)
return s | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/metrics/python/ops/metric_ops.py | python | precision_at_recall | (labels,
predictions,
target_recall,
weights=None,
num_thresholds=200,
metrics_collections=None,
updates_collections=None,
name=None) | Computes the precision at a given recall.
This function creates variables to track the true positives, false positives,
true negatives, and false negatives at a set of thresholds. Among those
thresholds where recall is at least `target_recall`, precision is computed
at the threshold where recall is closest to `target_recall`.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
precision at `target_recall`. `update_op` increments the counts of true
positives, false positives, true negatives, and false negatives with the
weight of each case found in the `predictions` and `labels`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
For additional information about precision and recall, see
http://en.wikipedia.org/wiki/Precision_and_recall
Args:
labels: The ground truth values, a `Tensor` whose dimensions must match
`predictions`. Will be cast to `bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
target_recall: A scalar value in range `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
num_thresholds: The number of thresholds to use for matching the given
recall.
metrics_collections: An optional list of collections to which `precision`
should be added.
updates_collections: An optional list of collections to which `update_op`
should be added.
name: An optional variable_scope name.
Returns:
precision: A scalar `Tensor` representing the precision at the given
`target_recall` value.
update_op: An operation that increments the variables for tracking the
true positives, false positives, true negatives, and false negatives and
whose value matches `precision`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, if
`weights` is not `None` and its shape doesn't match `predictions`, or if
`target_recall` is not between 0 and 1, or if either `metrics_collections`
or `updates_collections` are not a list or tuple.
RuntimeError: If eager execution is enabled. | Computes the precision at a given recall. | [
"Computes",
"the",
"precision",
"at",
"a",
"given",
"recall",
"."
] | def precision_at_recall(labels,
predictions,
target_recall,
weights=None,
num_thresholds=200,
metrics_collections=None,
updates_collections=None,
name=None):
"""Computes the precision at a given recall.
This function creates variables to track the true positives, false positives,
true negatives, and false negatives at a set of thresholds. Among those
thresholds where recall is at least `target_recall`, precision is computed
at the threshold where recall is closest to `target_recall`.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
precision at `target_recall`. `update_op` increments the counts of true
positives, false positives, true negatives, and false negatives with the
weight of each case found in the `predictions` and `labels`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
For additional information about precision and recall, see
http://en.wikipedia.org/wiki/Precision_and_recall
Args:
labels: The ground truth values, a `Tensor` whose dimensions must match
`predictions`. Will be cast to `bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
target_recall: A scalar value in range `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
num_thresholds: The number of thresholds to use for matching the given
recall.
metrics_collections: An optional list of collections to which `precision`
should be added.
updates_collections: An optional list of collections to which `update_op`
should be added.
name: An optional variable_scope name.
Returns:
precision: A scalar `Tensor` representing the precision at the given
`target_recall` value.
update_op: An operation that increments the variables for tracking the
true positives, false positives, true negatives, and false negatives and
whose value matches `precision`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, if
`weights` is not `None` and its shape doesn't match `predictions`, or if
`target_recall` is not between 0 and 1, or if either `metrics_collections`
or `updates_collections` are not a list or tuple.
RuntimeError: If eager execution is enabled.
"""
if context.executing_eagerly():
raise RuntimeError('tf.metrics.precision_at_recall is not '
'supported when eager execution is enabled.')
if target_recall < 0 or target_recall > 1:
raise ValueError('`target_recall` must be in the range [0, 1].')
with variable_scope.variable_scope(name, 'precision_at_recall',
(predictions, labels, weights)):
kepsilon = 1e-7 # Used to avoid division by zero.
thresholds = [
(i + 1) * 1.0 / (num_thresholds - 1) for i in range(num_thresholds - 2)
]
thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon]
values, update_ops = _streaming_confusion_matrix_at_thresholds(
predictions, labels, thresholds, weights)
def compute_precision_at_recall(tp, fp, fn, name):
"""Computes the precision at a given recall.
Args:
tp: True positives.
fp: False positives.
fn: False negatives.
name: A name for the operation.
Returns:
The precision at the desired recall.
"""
recalls = math_ops.div(tp, tp + fn + kepsilon)
# Because recall is monotone decreasing as a function of the threshold,
# the smallest recall exceeding target_recall occurs at the largest
# threshold where recall >= target_recall.
admissible_recalls = math_ops.cast(
math_ops.greater_equal(recalls, target_recall), dtypes.int64)
tf_index = math_ops.reduce_sum(admissible_recalls) - 1
# Now we have the threshold at which to compute precision:
return math_ops.div(tp[tf_index] + kepsilon,
tp[tf_index] + fp[tf_index] + kepsilon, name)
precision_value = compute_precision_at_recall(values['tp'], values['fp'],
values['fn'], 'value')
update_op = compute_precision_at_recall(update_ops['tp'], update_ops['fp'],
update_ops['fn'], 'update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, precision_value)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return precision_value, update_op | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/symbol/numpy/_symbol.py | python | flipud | (m) | return flip(m, 0) | r"""
flipud(*args, **kwargs)
Flip array in the up/down direction.
Flip the entries in each column in the up/down direction.
Rows are preserved, but appear in a different order than before.
Parameters
----------
m : array_like
Input array.
Returns
-------
out : array_like
A view of `m` with the rows reversed. Since a view is
returned, this operation is :math:`\mathcal O(1)`. | r"""
flipud(*args, **kwargs) | [
"r",
"flipud",
"(",
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] | def flipud(m):
r"""
flipud(*args, **kwargs)
Flip array in the up/down direction.
Flip the entries in each column in the up/down direction.
Rows are preserved, but appear in a different order than before.
Parameters
----------
m : array_like
Input array.
Returns
-------
out : array_like
A view of `m` with the rows reversed. Since a view is
returned, this operation is :math:`\mathcal O(1)`.
"""
return flip(m, 0) | [
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/dem/pair.py | python | _DEMBase.setParams2D | (self, type, vertices, center=False) | Set the vertices for a given particle type.
Args:
type (str): Name of the type to set the shape of
vertices (list): List of (2D) points specifying the coordinates of the shape
center (bool): If True, subtract the center of mass of the shape from the vertices before setting them for the shape
Shapes are specified as a list of 2D coordinates. Edges will
be made between all adjacent pairs of vertices, including one
between the last and first vertex. | Set the vertices for a given particle type. | [
"Set",
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"for",
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"given",
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"type",
"."
] | def setParams2D(self, type, vertices, center=False):
"""Set the vertices for a given particle type.
Args:
type (str): Name of the type to set the shape of
vertices (list): List of (2D) points specifying the coordinates of the shape
center (bool): If True, subtract the center of mass of the shape from the vertices before setting them for the shape
Shapes are specified as a list of 2D coordinates. Edges will
be made between all adjacent pairs of vertices, including one
between the last and first vertex.
"""
itype = hoomd.context.current.system_definition.getParticleData(
).getTypeByName(type)
if not len(vertices):
vertices = [(0, 0)]
center = False
# explicitly turn into a list of tuples
if center:
vertices = [
(float(p[0]), float(p[1])) for p in utils.center(vertices)
]
else:
vertices = [(float(p[0]), float(p[1])) for p in vertices]
# update the neighbor list
rcutmax = 2 * (sqrt(max(x * x + y * y for (x, y) in vertices))
+ self.radius * 2**(1. / 6))
self.r_cut = max(self.r_cut, rcutmax)
self.vertices[type] = vertices
self.cpp_force.setRcut(self.r_cut)
self.cpp_force.setParams(itype, vertices) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/valgrind/suppressions.py | python | ReadValgrindStyleSuppressions | (lines, supp_descriptor) | return result | Given a list of lines, returns a list of suppressions.
Args:
lines: a list of lines containing suppressions.
supp_descriptor: should typically be a filename.
Used only when printing errors. | Given a list of lines, returns a list of suppressions. | [
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"returns",
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"suppressions",
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] | def ReadValgrindStyleSuppressions(lines, supp_descriptor):
"""Given a list of lines, returns a list of suppressions.
Args:
lines: a list of lines containing suppressions.
supp_descriptor: should typically be a filename.
Used only when printing errors.
"""
result = []
cur_descr = ''
cur_type = ''
cur_stack = []
in_suppression = False
nline = 0
for line in lines:
nline += 1
line = line.strip()
if line.startswith('#'):
continue
if not in_suppression:
if not line:
# empty lines between suppressions
pass
elif line.startswith('{'):
in_suppression = True
pass
else:
raise SuppressionError('Expected: "{"',
"%s:%d" % (supp_descriptor, nline))
elif line.startswith('}'):
result.append(
ValgrindStyleSuppression(cur_descr, cur_type, cur_stack,
"%s:%d" % (supp_descriptor, nline)))
cur_descr = ''
cur_type = ''
cur_stack = []
in_suppression = False
elif not cur_descr:
cur_descr = line
continue
elif not cur_type:
if (not line.startswith("Memcheck:") and
not line.startswith("ThreadSanitizer:")):
raise SuppressionError(
'Expected "Memcheck:TYPE" or "ThreadSanitizer:TYPE", '
'got "%s"' % line,
"%s:%d" % (supp_descriptor, nline))
supp_type = line.split(':')[1]
if not supp_type in ["Addr1", "Addr2", "Addr4", "Addr8",
"Cond", "Free", "Jump", "Leak", "Overlap", "Param",
"Value1", "Value2", "Value4", "Value8",
"Race", "UnlockNonLocked", "InvalidLock",
"Unaddressable", "Uninitialized"]:
raise SuppressionError('Unknown suppression type "%s"' % supp_type,
"%s:%d" % (supp_descriptor, nline))
cur_type = line
continue
elif re.match("^fun:.*|^obj:.*|^\.\.\.$", line):
cur_stack.append(line.strip())
elif len(cur_stack) == 0 and cur_type == "Memcheck:Param":
cur_stack.append(line.strip())
else:
raise SuppressionError(
'"fun:function_name" or "obj:object_file" or "..." expected',
"%s:%d" % (supp_descriptor, nline))
return result | [
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ablab/spades | 3a754192b88540524ce6fb69eef5ea9273a38465 | webvis/pydot.py | python | graph_from_dot_file | (path) | return graph_from_dot_data(data) | Load graph as defined by a DOT file.
The file is assumed to be in DOT format. It will
be loaded, parsed and a Dot class will be returned,
representing the graph. | Load graph as defined by a DOT file.
The file is assumed to be in DOT format. It will
be loaded, parsed and a Dot class will be returned,
representing the graph. | [
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"""Load graph as defined by a DOT file.
The file is assumed to be in DOT format. It will
be loaded, parsed and a Dot class will be returned,
representing the graph.
"""
fd = file(path, 'rb')
data = fd.read()
fd.close()
return graph_from_dot_data(data) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/__init__.py | python | connect_cloudtrail | (aws_access_key_id=None,
aws_secret_access_key=None,
**kwargs) | return CloudTrailConnection(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
**kwargs
) | Connect to AWS CloudTrail
:type aws_access_key_id: string
:param aws_access_key_id: Your AWS Access Key ID
:type aws_secret_access_key: string
:param aws_secret_access_key: Your AWS Secret Access Key
:rtype: :class:`boto.cloudtrail.layer1.CloudtrailConnection`
:return: A connection to the AWS Cloudtrail service | Connect to AWS CloudTrail | [
"Connect",
"to",
"AWS",
"CloudTrail"
] | def connect_cloudtrail(aws_access_key_id=None,
aws_secret_access_key=None,
**kwargs):
"""
Connect to AWS CloudTrail
:type aws_access_key_id: string
:param aws_access_key_id: Your AWS Access Key ID
:type aws_secret_access_key: string
:param aws_secret_access_key: Your AWS Secret Access Key
:rtype: :class:`boto.cloudtrail.layer1.CloudtrailConnection`
:return: A connection to the AWS Cloudtrail service
"""
from boto.cloudtrail.layer1 import CloudTrailConnection
return CloudTrailConnection(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
**kwargs
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Menu.IsChecked | (*args, **kwargs) | return _core_.Menu_IsChecked(*args, **kwargs) | IsChecked(self, int id) -> bool | IsChecked(self, int id) -> bool | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/math/symbolic.py | python | Function.info | (self) | return '\n'.join(items) | Returns an text string describing the Function, similar to a docstring | Returns an text string describing the Function, similar to a docstring | [
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] | def info(self):
"""Returns an text string describing the Function, similar to a docstring"""
argstr = '...' if self.argNames is None else ','.join(self.argNames)
signature = '%s(%s)'%(self.name,argstr)
if isinstance(self.func,Expression):
signature = signature + '\n Defined as '+str(self.func)
argHelp = None
if self.argTypes is not None or self.argDescriptions is not None:
if self.argNames is None:
argHelp = [str(self.argDescriptions)]
else:
argHelp= []
for i,name in enumerate(self.argNames):
type = None if self.argTypes is None else self.argTypes[i]
desc = None if self.argDescriptions is None else self.argDescriptions[i]
if desc is None:
if type is not None:
desc = type.info()
else:
desc = 'unknown'
argHelp.append('- %s: %s'%(name,desc))
returnHelp = None
if self.returnTypeDescription is not None:
returnHelp = "- " + self.returnTypeDescription
elif self.returnType is not None:
returnHelp = "- " + self.returnType.info()
elif self.returnTypeFunc is not None:
if self.returnTypeFunc == _propagate_returnType:
returnHelp = "- same as arguments"
elif self.returnTypeFunc == _promote_returnType:
returnHelp = "- same as arguments"
elif self.returnTypeFunc == _returnType1:
returnHelp = "- same as argument 1"
elif self.returnTypeFunc == _returnType2:
returnHelp = "- same as argument 2"
elif self.returnTypeFunc == _returnType3:
returnHelp = "- same as argument 3"
else:
returnHelp = "- dynamic"
derivHelp = None
if self.deriv is not None or self.jacobian is not None:
derivHelp = []
if _is_exactly(self.deriv,0):
derivHelp.append('- derivative is 0 everywhere')
elif callable(self.deriv):
deval = None
if self.argNames is not None:
argTypes = [Type(None)]*len(self.argNames) if self.argTypes is None else self.argTypes
vars = [expr(Variable(a,t)) for a,t in zip(self.argNames,argTypes)]
dvars = [expr(Variable('d'+a,t)) for a,t in zip(self.argNames,argTypes)]
try:
deval = self.deriv(vars,dvars)
except Exception:
pass
if deval:
derivHelp.append('- derivative is '+str(deval))
else:
derivHelp.append('- derivative is a total derivative function')
elif callable(self.jacobian):
derivHelp.append('- jacobian is a total derivative function')
elif self.argNames is not None:
argTypes = [Type(None)]*len(self.argNames) if self.argTypes is None else self.argTypes
vars = [expr(Variable(a,t)) for a,t in zip(self.argNames,argTypes)]
for i,a in enumerate(self.argNames):
if self.deriv is not None and self.deriv[i] is not None:
if _is_exactly(self.deriv[i],0):
derivHelp.append('- %s: derivative is 0'%(a,))
elif isinstance(self.deriv[i],Function):
derivHelp.append('- %s: available as Python df/da * da/dx function'%(a,))
else:
try:
deval = self.deriv[i](*(vars+[expr(Variable('d'+a,argTypes[i]))]))
if is_op(deval,'subs'):
deval = deval.args[0]
derivHelp.append('- %s: available as df/da * da/dx function %s'%(a,str(deval)))
except Exception as e:
derivHelp.append('- %s: available as df/da * da/dx function, Exception %s'%(a,str(e)))
elif self.jacobian is not None and self.jacobian[i] is not None:
if _is_exactly(self.jacobian[i],0):
derivHelp.append('- %s: jacobian is 0'%(a,))
elif isinstance(self.deriv[i],Function):
derivHelp.append('- %s: available as Python jacobian df/da'%(a,))
else:
try:
deval = self.jacobian[i](*vars)
if is_op(deval,'subs'):
deval = deval.args[0]
derivHelp.append('- %s: available as jacobian df/da %s'%(a,str(deval)))
except Exception as e:
derivHelp.append('- %s: available as jacobian df/da, Exception %s'%(a,str(e)))
items = [signature]
if self.description != None:
items += ['',self.description,'']
if argHelp is not None and len(argHelp) > 0:
items += ['','Parameters','---------']+argHelp
if returnHelp is not None:
items += ['','Return type','-----------',returnHelp]
if derivHelp is not None and len(derivHelp) > 0:
items += ['','Derivatives','-----------']+derivHelp
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Event.Skip | (*args, **kwargs) | return _core_.Event_Skip(*args, **kwargs) | Skip(self, bool skip=True)
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current one returns. Without Skip() (or equivalently if Skip(False) is
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/Chem/ChemUtils/AlignDepict.py | python | main | () | Main application | Main application | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/util/nest.py | python | list_to_tuple | (structure) | return _pack_sequence_as(structure, flatten(structure), False,
sequence_fn=sequence_fn) | Replace all lists with tuples.
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"""Replace all lists with tuples.
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the latter convention, and must therefore deeply replace all lists with tuples
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Args:
structure: A nested structure to be remapped.
Returns:
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"""
def sequence_fn(instance, args):
if isinstance(instance, list):
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return _pack_sequence_as(structure, flatten(structure), False,
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numworks/epsilon | 8952d2f8b1de1c3f064eec8ffcea804c5594ba4c | build/device/usb/backend/__init__.py | python | IBackend.release_interface | (self, dev_handle, intf) | r"""Release the claimed interface.
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gnuradio-runtime/python/gnuradio/gr/top_block.py | python | top_block.unlock | (self) | Release lock and continue execution of flow-graph. | Release lock and continue execution of flow-graph. | [
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Release lock and continue execution of flow-graph.
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/build/lib/google/protobuf/internal/encoder.py | python | StringSizer | (field_number, is_repeated, is_packed) | Returns a sizer for a string field. | Returns a sizer for a string field. | [
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] | def StringSizer(field_number, is_repeated, is_packed):
"""Returns a sizer for a string field."""
tag_size = _TagSize(field_number)
local_VarintSize = _VarintSize
local_len = len
assert not is_packed
if is_repeated:
def RepeatedFieldSize(value):
result = tag_size * len(value)
for element in value:
l = local_len(element.encode('utf-8'))
result += local_VarintSize(l) + l
return result
return RepeatedFieldSize
else:
def FieldSize(value):
l = local_len(value.encode('utf-8'))
return tag_size + local_VarintSize(l) + l
return FieldSize | [
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luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0149-Max-Points-on-a-Line/0149.py | python | Solution.maxPoints | (self, points) | return result | :type points: List[Point]
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"""
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continue
slope = float('inf') if point1.x == point2.x else \
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slopes[slope] += 1
if result < duplicate:
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for _, val in slopes.items():
if val + duplicate > result:
result = val + duplicate
return result | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py | python | ExponentialUpdateLossScaleManager.__init__ | (self,
init_loss_scale,
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decr_every_n_nan_or_inf=2,
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decr_ratio=0.8) | Constructor of exponential-update loss scale manager.
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init_loss_scale: A Python float. The loss scale to use at the beginning.
incr_every_n_steps: Increases loss scale every n consecutive steps with
finite gradients.
decr_every_n_nan_or_inf: Decreases loss scale every n accumulated steps
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decr_ratio=0.8):
"""Constructor of exponential-update loss scale manager.
Args:
init_loss_scale: A Python float. The loss scale to use at the beginning.
incr_every_n_steps: Increases loss scale every n consecutive steps with
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decr_every_n_nan_or_inf: Decreases loss scale every n accumulated steps
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"""
self._incr_every_n_steps = incr_every_n_steps
self._decr_every_n_nan_or_inf = decr_every_n_nan_or_inf
self._incr_ratio = incr_ratio
self._decr_ratio = decr_ratio
self._loss_scale = variable_scope.variable(
name="loss_scale",
initial_value=ops.convert_to_tensor(init_loss_scale, dtypes.float32),
dtype=dtypes.float32,
trainable=False)
self._num_good_steps = variable_scope.variable(
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self._num_bad_steps = variable_scope.variable(
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apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | dev/archery/archery/utils/git.py | python | Git.repository_root | (self, git_dir=None, **kwargs) | return stdout.decode('utf-8') | Locates the repository's root path from a subdirectory. | Locates the repository's root path from a subdirectory. | [
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Ardour/ardour | a63a18a3387b90c0920d9b1668d2a50bd6302b83 | tools/cstyle.py | python | Preprocessor.process_strings | (self, line) | return line | Given a line of C code, return a string where all literal C strings have
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ziquan111/RobustPCLReconstruction | 35b9518dbf9ad3f06109cc0e3aaacafdb5c86e36 | py/sophus/quaternion.py | python | Quaternion.conj | (self) | return Quaternion(self.real, -self.vec) | quaternion conjugate | quaternion conjugate | [
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""" quaternion conjugate """
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | examples/python/symbolication.py | python | Image.create_target | (self) | return None | Create a target using the information in this Image object. | Create a target using the information in this Image object. | [
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if err:
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/turtle.py | python | _getscreen | () | return Turtle._screen | Create a TurtleScreen if not already present. | Create a TurtleScreen if not already present. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/cython/Cython/Plex/DFA.py | python | StateMap.new_to_old | (self, new_state) | return self.new_to_old_dict[id(new_state)] | Given a new state, return a set of corresponding old states. | Given a new state, return a set of corresponding old states. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/boringssl/src/util/generate_build_files.py | python | FindCMakeFiles | (directory) | return cmakefiles | Returns list of all CMakeLists.txt files recursively in directory. | Returns list of all CMakeLists.txt files recursively in directory. | [
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cmakefiles = []
for (path, _, filenames) in os.walk(directory):
for filename in filenames:
if filename == 'CMakeLists.txt':
cmakefiles.append(os.path.join(path, filename))
return cmakefiles | [
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/sping/PDF/pidPDF.py | python | PDFCanvas._updateLineWidth | (self, width) | Triggered when someone assigns to defaultLineWidth | Triggered when someone assigns to defaultLineWidth | [
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] | def _updateLineWidth(self, width):
"""Triggered when someone assigns to defaultLineWidth"""
self.pdf.setLineWidth(width) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | TextAreaBase.ShowPosition | (*args, **kwargs) | return _core_.TextAreaBase_ShowPosition(*args, **kwargs) | ShowPosition(self, long pos) | ShowPosition(self, long pos) | [
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mandiant/flare-wmi | b0a5a094ff9ca7d7a1c4fc711dc00c74dec4b6b1 | python-cim/cim/cim.py | python | LogicalIndexStore.root_page_number | (self) | fetch the logical page number of the index root.
Returns:
int: the logical page number. | fetch the logical page number of the index root.
Returns:
int: the logical page number. | [
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"""
fetch the logical page number of the index root.
Returns:
int: the logical page number.
"""
if self._cim.cim_type == CIM_TYPE_WIN7:
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raise RuntimeError("Unexpected CIM type: " + str(self._cim.cim_type)) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/utils/data_utils.py | python | next_sample | (uid) | return next(_SHARED_SEQUENCES[uid]) | Gets the next value from the generator `uid`.
To allow multiple generators to be used at the same time, we use `uid` to
get a specific one. A single generator would cause the validation to
overwrite the training generator.
Args:
uid: int, generator identifier
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"""Gets the next value from the generator `uid`.
To allow multiple generators to be used at the same time, we use `uid` to
get a specific one. A single generator would cause the validation to
overwrite the training generator.
Args:
uid: int, generator identifier
Returns:
The next value of generator `uid`.
"""
return next(_SHARED_SEQUENCES[uid]) | [
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clasp-developers/clasp | 5287e5eb9bbd5e8da1e3a629a03d78bd71d01969 | tools-for-build/pump.py | python | SubString | (lines, start, end) | return ''.join(result_lines) | Returns a substring in lines. | Returns a substring in lines. | [
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result_lines = ([lines[start.line][start.column:]] +
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[lines[end.line][:end.column]])
return ''.join(result_lines) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/lookup_ops.py | python | LookupInterface.__init__ | (self, key_dtype, value_dtype) | Construct a lookup table interface.
Args:
key_dtype: The table key type.
value_dtype: The table value type. | Construct a lookup table interface. | [
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"""Construct a lookup table interface.
Args:
key_dtype: The table key type.
value_dtype: The table value type.
"""
self._key_dtype = dtypes.as_dtype(key_dtype)
self._value_dtype = dtypes.as_dtype(value_dtype)
super(LookupInterface, self).__init__() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/http/server.py | python | SimpleHTTPRequestHandler.list_directory | (self, path) | return f | Helper to produce a directory listing (absent index.html).
Return value is either a file object, or None (indicating an
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"""Helper to produce a directory listing (absent index.html).
Return value is either a file object, or None (indicating an
error). In either case, the headers are sent, making the
interface the same as for send_head().
"""
try:
list = os.listdir(path)
except OSError:
self.send_error(
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return None
list.sort(key=lambda a: a.lower())
r = []
try:
displaypath = urllib.parse.unquote(self.path,
errors='surrogatepass')
except UnicodeDecodeError:
displaypath = urllib.parse.unquote(path)
displaypath = html.escape(displaypath, quote=False)
enc = sys.getfilesystemencoding()
title = 'Directory listing for %s' % displaypath
r.append('<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" '
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r.append('<html>\n<head>')
r.append('<meta http-equiv="Content-Type" '
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r.append('<title>%s</title>\n</head>' % title)
r.append('<body>\n<h1>%s</h1>' % title)
r.append('<hr>\n<ul>')
for name in list:
fullname = os.path.join(path, name)
displayname = linkname = name
# Append / for directories or @ for symbolic links
if os.path.isdir(fullname):
displayname = name + "/"
linkname = name + "/"
if os.path.islink(fullname):
displayname = name + "@"
# Note: a link to a directory displays with @ and links with /
r.append('<li><a href="%s">%s</a></li>'
% (urllib.parse.quote(linkname,
errors='surrogatepass'),
html.escape(displayname, quote=False)))
r.append('</ul>\n<hr>\n</body>\n</html>\n')
encoded = '\n'.join(r).encode(enc, 'surrogateescape')
f = io.BytesIO()
f.write(encoded)
f.seek(0)
self.send_response(HTTPStatus.OK)
self.send_header("Content-type", "text/html; charset=%s" % enc)
self.send_header("Content-Length", str(len(encoded)))
self.end_headers()
return f | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/mixed_precision/amp_nn.py | python | check_finite_and_unscale | (x, scale, name=None, float_status=None) | return x, found_inf | Check if input X contains all finite data, if yes, scale it by input Scale.
$$Out = X / scale$$
If any tensor in X contains Inf or Nan, the Out will generate a indicator.
FoundInfinite will be 1 (True), and Out will not be scaled. In this case, the data of
Out should not be used, and its data may not be deterministic.
Otherwise, FoundInfinite will be 0 (False).
Args:
x(list|tuple): The input tensors of check_finite_and_unscale operator.
scale: The scale of check_finite_and_unscale operator.
float_status(Tensor): (Only used on NPU) The float status to check overflow. | Check if input X contains all finite data, if yes, scale it by input Scale. | [
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] | def check_finite_and_unscale(x, scale, name=None, float_status=None):
"""
Check if input X contains all finite data, if yes, scale it by input Scale.
$$Out = X / scale$$
If any tensor in X contains Inf or Nan, the Out will generate a indicator.
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Args:
x(list|tuple): The input tensors of check_finite_and_unscale operator.
scale: The scale of check_finite_and_unscale operator.
float_status(Tensor): (Only used on NPU) The float status to check overflow.
"""
check_type(x, 'x', (tuple, list), 'check_finite_and_unscale')
for e in x:
check_variable_and_dtype(e, "x", ['float16', 'float32', 'float64'],
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helper = LayerHelper("check_finite_and_unscale", **locals())
found_inf = helper.create_variable_for_type_inference(dtype='bool')
inputs = {'X': x, 'Scale': scale}
if core.is_compiled_with_npu():
check_variable_and_dtype(float_status, "float_status",
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inputs['FloatStatus'] = float_status
outputs = {'Out': x, 'FoundInfinite': found_inf}
helper.append_op(
type='check_finite_and_unscale', inputs=inputs, outputs=outputs)
return x, found_inf | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/sipconfig.py | python | create_config_module | (module, template, content, macros=None) | Create a configuration module by replacing "@" followed by
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module is the name of the module file.
template is the name of the template file.
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macros is an optional dictionary of platform specific build macros. It is
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"""Create a configuration module by replacing "@" followed by
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string.
module is the name of the module file.
template is the name of the template file.
content is the content string. If it is a dictionary it is first converted
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if type(content) == dict:
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# Allow this file to used as a template.
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line = sf.readline()
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df.write(line)
line = sf.readline()
df.close()
sf.close() | [
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